Dialogue management in vector-based call routing
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance
User Modeling and User-Adapted Interaction
Targeted help for spoken dialogue systems: intelligent feedback improves naive users' performance
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
Multi-domain spoken language understanding with transfer learning
Speech Communication
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Spoken dialogue systems must inevitably deal with out-of-grammar utterances. We address this problem in multi-domain spoken dialogue systems, which deal with more tasks than a single-domain system. We defined a topic by augmenting a domain about which users want to find more information, and we developed a method of recovering out-of-grammar utterances based on topic estimation, i.e., by providing a help message in the estimated domain. Moreover, domain extensibility, that is, the ability to add new domains to the system, should be inherently retained in multi-domain systems. To estimate domains without sacrificing extensibility, we collected documents from the Web as training data. Since the data contained a certain amount of noise, we used latent semantic mapping (LSM), which enables robust topic estimation by removing the effects of noise from the data. Experimental results showed that our method improved topic estimation accuracy by 23.2 points for data including out-of-grammar utterances.